Nuance Communications, Inc.

Biamp Systems Corporation

Cambridge Sound Management, Inc.

Robert Fleming

William F Ganong, Iii

Raghu Vemula

Christopher Calisi

Faruk H Bursal

Faruk Halil Bursal

Gordon V Cook

Gregory Saunders

Faruk Bursal

Example embodiments may include one or more of receiving sound emissions signals from channels via sound emitters, controlling the sound emission signals, via relay circuits, and one of the relay circuits is configured to interrupt one of the sound emission signals associated with one of the sound emitters while the other sound emissions signals pass to the other corresponding sound emitters, and receiving, via a sound detection circuit, an electrical ambient sound signal based on ambient sound sensed by the one of the sound emitters responsive to the interrupted one of the sound emission signals.

Acoustic monitoring using a sound masking emitter as a sensor

Example embodiments may include one or more of receiving an electrical sound emission signal from a sound controller, interrupting reception of the electrical sound emission signal, by a sound emission interruption circuit connected to a sound emitter, and receiving an electrical ambient sound signal via a sound detection circuit, based on ambient sound sensed by the sound emitter when the reception of the electrical sound emission signal is interrupted by the sound emission interruption circuit.

A commercial lighting system, the system comprising, an integral housing adapted to be coupled to a ceiling of a building, a lighting unit mounted to the integral housing and a loudspeaker assembly mounted to the integral housing, the loudspeaker assembly comprising a low directivity index, and oriented to provide an acoustic sound signal from the integral housing to a listener in an area of the building comprising the ceiling.

Commercial lighting integrated with loudspeakers for sound masking, paging or music

In some embodiments, recognition results produced by a speech processing system (which may include two or more recognition results, including a top recognition result and one or more alternative recognition results) based on an analysis of a speech input, are evaluated for indications of potential errors. In some embodiments, the indications of potential errors may include discrepancies between recognition results that are meaningful for a domain, such as medically-meaningful discrepancies. The evaluation of the recognition results may be carried out using any suitable criteria, including one or more criteria that differ from criteria used by an ASR system in determining the top recognition result and the alternative recognition results from the speech input. In some embodiments, a recognition result may additionally or alternatively be processed to determine whether the recognition result includes a word or phrase that is unlikely to appear in a domain to which speech input relates.

Detecting potential significant errors in speech recognition results

In some embodiments, the recognition results produced by a speech processing system (which may include a top recognition result and one or more alternative recognition results) based on an analysis of a speech input, are evaluated for indications of potential significant errors. In some embodiments, the recognition results may be evaluated to determine whether a meaning of any of the alternative recognition results differs from a meaning of the top recognition result in a manner that is significant for the domain. In some embodiments, one or more of the recognition results may be evaluated to determine whether the result(s) include one or more words or phrases that, when included in a result, would change a meaning of the result in a manner that would be significant for the domain.

In some embodiments, a recognition result produced by a speech processing system based on an analysis of a speech input is evaluated for indications of potential errors. In some embodiments, sets of words/phrases that may be acoustically similar or otherwise confusable, the misrecognition of which can be significant in the domain, may be used together with a language model to evaluate a recognition result to determine whether the recognition result includes such an indication. In some embodiments, a word/phrase of a set that appears in the result is iteratively replaced with each of the other words/phrases of the set. The result of the replacement may be evaluated using a language model to determine a likelihood of the newly-created string of words appearing in a language and/or domain. The likelihood may then be evaluated to determine whether the result of the replacement is sufficiently likely for an alert to be triggered.

Using models to detect potential significant errors in speech recognition results

In some embodiments, the recognition results produced by a speech processing system (which may include two or more recognition results, including a top recognition result and one or more alternative recognition results) based on an analysis of a speech input, are evaluated for indications of potential significant errors. In some embodiments, the recognition results may be evaluated to determine whether a meaning of any of the alternative recognition results differs from a meaning of the top recognition result in a manner that is significant for a domain, such as the medical domain. In some embodiments, words and/or phrases that may be confused by an ASR system may be determined and associated in sets of words and/or phrases. Words and/or phrases that may be determined include those that change a meaning of a phrase or sentence when included in the phrase/sentence.

Detecting potential medically-significant errors in speech recognition results

In some embodiments, the recognition results produced by a speech processing system (which may include two or more recognition results, including a top recognition result and one or more alternative recognition results) based on an analysis of a speech input, are evaluated for indications of potential significant errors. In some embodiments, the recognition results may be evaluated using one or more sets of words and/or phrases, such as pairs of words/phrases that may include words/phrases that are acoustically similar to one another and/or that, when included in a result, would change a meaning of the result in a manner that would be significant for a domain. The recognition results may be evaluated using the set(s) of words/phrases to determine, when the top result includes a word/phrase from a set of words/phrases, whether any of the alternative recognition results includes any of the other, corresponding words/phrases from the set.

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Lynnfield, MA, United States of America

Robert Fleming